Meta-learning is an alternative to the de-facto solution that has emerged in deep learning of pre-training a network using a large, generic dataset (eg ImageNet [1]}) followed by fine-tuning with a problem-specific dataset. Meta-learning aims to replace the fine-tuning stage (which can still be very expensive) by training a network that has a degree of plasticity so that it can adapt rapidly to new tasks. For this reason it has become a very active area recently, especially with regard to one-shot and few-shot learning problems [2]}, [3]}
